基于混合蛙跳和遗传规划的跨单元调度方法

针对运输能力受限条件下的跨单元问题,提出了一种基于混合蛙跳与遗传规划的超启发式算法.将改进的混合蛙跳算法作为超启发式算法的高层框架,为跨单元调度问题搜索启发式规则,同时利用遗传规划产生可以兼顾多因素的优质规则,用于扩充超启发式算法的规则集.实验表明,提出的算法可以有效地搜索出优异的规则组合,并且通过遗传规划产生的规则可以在很大程度上改善候选规则集,提升算法性能.

An Intercell Scheduling Approach Using Shuffled Frog Leaping Algorithm and Genetic Programming

To deal with the intercell scheduling problem with limited transportation capabilities, a shuffled frog leaping algorithm-based hyperheuristic approach with genetic programming is developed in this paper. The proposed approach develops an improved shuffled frog leaping algorithm to search the combinations of heuristic rules for the addressed problem. Meanwhile, genetic programming is also introduced into the proposed algorithm to generate well-performing heuristic rules as an extension to the predefined candidate heuristic rules. Experimental results show that the improved shuffled frog leaping algorithm is efficient to search the outperforming combinations of the heuristic rules, and that the heuristic rules generated via genetic programming can obviously improve the quality of the candidate rule set, therefore providing better performance.